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Datasets:
ry-02
/
github-agentic-workflows-dataset

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Spanish
Size:
< 1K
Tags:
github
workflows
parquet
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Dataset Viewer
Auto-converted to Parquet Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
file_id: large_string
repository_id: int64
file_name: large_string
file_path: large_string
body_markdown: large_string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 676
to
{'repository_id': Value('int64'), 'owner': Value('large_string'), 'name': Value('large_string'), 'full_name': Value('large_string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              file_id: large_string
              repository_id: int64
              file_name: large_string
              file_path: large_string
              body_markdown: large_string
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 676
              to
              {'repository_id': Value('int64'), 'owner': Value('large_string'), 'name': Value('large_string'), 'full_name': Value('large_string')}
              because column names don't match

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GitHub Agentic Workflows Dataset

Este dataset contiene la información extraída y estructurada de archivos Markdown (.github/workflows/*.md) pertenecientes a repositorios de GitHub que implementan Agentic Workflows.

Estructura del Dataset

El dataset se organiza en 3 tablas relacionales en formato Apache Parquet:

  1. repositories.parquet: Identificadores y nombres de los repositorios procesados.
  2. workflow_files.parquet: Archivos .md localizados, con su ruta original y el contenido en Markdown (body_markdown).
  3. workflow_metadata.parquet: Atributos parseados del Frontmatter en YAML de cada workflow (name, description, tools, etc.).

Ejemplo de uso en Python 

import pandas as pd

# Cargar las tablas desde Hugging Face
df_repos = pd.read_parquet("hf://datasets/ry-02/github-agentic-workflows-dataset/repositories.parquet")
df_files = pd.read_parquet("hf://datasets/ry-02/github-agentic-workflows-dataset/workflow_files.parquet")
df_meta = pd.read_parquet("hf://datasets/ry-02/github-agentic-workflows-dataset/workflow_metadata.parquet")

print(df_repos.head())
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